{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/andre/Downloads/water.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>location</th>\n",
       "      <th>town</th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "      <td>Bath</td>\n",
       "      <td>1247</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North</td>\n",
       "      <td>Birkenhead</td>\n",
       "      <td>1668</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>South</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1466</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>North</td>\n",
       "      <td>Blackburn</td>\n",
       "      <td>1800</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>North</td>\n",
       "      <td>Blackpool</td>\n",
       "      <td>1609</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>South</td>\n",
       "      <td>Walsall</td>\n",
       "      <td>1527</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>South</td>\n",
       "      <td>West Bromwich</td>\n",
       "      <td>1627</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>South</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1486</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>1485</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>North</td>\n",
       "      <td>York</td>\n",
       "      <td>1378</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 location           town  mortality  hardness\n",
       "0            1    South           Bath       1247       105\n",
       "1            2    North     Birkenhead       1668        17\n",
       "2            3    South     Birmingham       1466         5\n",
       "3            4    North      Blackburn       1800        14\n",
       "4            5    North      Blackpool       1609        18\n",
       "..         ...      ...            ...        ...       ...\n",
       "56          57    South        Walsall       1527        60\n",
       "57          58    South  West Bromwich       1627        53\n",
       "58          59    South       West Ham       1486       122\n",
       "59          60    South  Wolverhampton       1485        81\n",
       "60          61    North           York       1378        71\n",
       "\n",
       "[61 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_correaltion(x, y):\n",
    "    plt.scatter(x, y)\n",
    "    statistic_spearman, p_spearmen = stats.spearmanr(x, y)\n",
    "    statistic_pearson, p_pearson = stats.pearsonr(x, y)\n",
    "    if p_spearmen < 0.15:\n",
    "        print(f\"Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна {str(statistic_spearman)}\")\n",
    "    elif p_spearmen > 0.15:\n",
    "        print(f\"Корреляция Спирмена не имеет статистической значимости ( p > 0.15) и равна {str(statistic_spearman)}\")\n",
    "\n",
    "    if p_pearson < 0.15:\n",
    "        print(f\"Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна {str(statistic_pearson)}\")\n",
    "    elif p_pearson > 0.15:\n",
    "        print(f\"Корреляция Пирсона не имеет статистической значимости ( p > 0.15) и равна {str(statistic_pearson)}\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task #1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна -0.6316646189166502\n",
      "Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна -0.6548486232042466\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_correaltion(df.hardness, df.mortality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task#2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_south = df.loc[df.location == 'South',:]\n",
    "df_north = df.loc[df.location == 'North',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна -0.4042078956511175\n",
      "Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна -0.36859783832887194\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_correaltion(df_north.hardness, df_north.mortality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна -0.5957229185013566\n",
      "Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна -0.6021532715484156\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_correaltion(df_south.hardness, df_south.mortality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод: небольшая зависимость есть, график и тесты по всем регионам показывает наличие этой связи. Анализ по регионам почти не показал наличие зависимости.  Теоретически можно сказать что с увеличением жесткости воды смертность уменьшается"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
